{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**HyperTS supports univariate and multivariate time series forecsting tasks.**\n",
    "\n",
    "**In this NoteBook, we will use HyperTS to do a time series forecasting task.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Step 1: Let`s still use **Network Traffic** dataset, import it and do EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "from hyperts.datasets import load_network_traffic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = load_network_traffic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Var_2</th>\n",
       "      <th>Var_3</th>\n",
       "      <th>Var_4</th>\n",
       "      <th>Var_5</th>\n",
       "      <th>Var_6</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01 00:00:00</td>\n",
       "      <td>0.7534</td>\n",
       "      <td>3.375</td>\n",
       "      <td>10.195</td>\n",
       "      <td>1.4490</td>\n",
       "      <td>19174.977</td>\n",
       "      <td>286443.880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-01 01:00:00</td>\n",
       "      <td>0.3376</td>\n",
       "      <td>2.414</td>\n",
       "      <td>3.920</td>\n",
       "      <td>0.4065</td>\n",
       "      <td>7529.263</td>\n",
       "      <td>178930.450</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-01 02:00:00</td>\n",
       "      <td>0.2032</td>\n",
       "      <td>1.654</td>\n",
       "      <td>3.318</td>\n",
       "      <td>0.2142</td>\n",
       "      <td>3310.539</td>\n",
       "      <td>42296.164</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-01 03:00:00</td>\n",
       "      <td>0.2420</td>\n",
       "      <td>1.393</td>\n",
       "      <td>3.148</td>\n",
       "      <td>0.2312</td>\n",
       "      <td>4535.464</td>\n",
       "      <td>26220.232</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-01 04:00:00</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>1.429</td>\n",
       "      <td>3.215</td>\n",
       "      <td>0.2157</td>\n",
       "      <td>2732.911</td>\n",
       "      <td>27990.348</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp   Var_1  Var_2   Var_3   Var_4      Var_5       Var_6  \\\n",
       "0  2021-03-01 00:00:00  0.7534  3.375  10.195  1.4490  19174.977  286443.880   \n",
       "1  2021-03-01 01:00:00  0.3376  2.414   3.920  0.4065   7529.263  178930.450   \n",
       "2  2021-03-01 02:00:00  0.2032  1.654   3.318  0.2142   3310.539   42296.164   \n",
       "3  2021-03-01 03:00:00  0.2420  1.393   3.148  0.2312   4535.464   26220.232   \n",
       "4  2021-03-01 04:00:00  0.1940  1.429   3.215  0.2157   2732.911   27990.348   \n",
       "\n",
       "    HourSin  WeekCos CBWD  \n",
       "0  0.000000      1.0   NW  \n",
       "1  0.258819      1.0   NW  \n",
       "2  0.500000      1.0   NW  \n",
       "3  0.707107      1.0   NW  \n",
       "4  0.866025      1.0   NW  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table, we can see that:\n",
    "- timestamp column name: 'TimeStamp';\n",
    "- target column names: ['Var_1', 'Var_2', 'Var_3', 'Var_4', 'Var_5', 'Var_6'];\n",
    "- covariable column names: ['HourSin', 'WeekCos', 'CBWD'];\n",
    "- time freq: 'H'.\n",
    "\n",
    "According to the value of the target variables, there are dimensional differences between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2878 entries, 0 to 2877\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TimeStamp  2878 non-null   object \n",
      " 1   Var_1      2878 non-null   float64\n",
      " 2   Var_2      2878 non-null   float64\n",
      " 3   Var_3      2877 non-null   float64\n",
      " 4   Var_4      2877 non-null   float64\n",
      " 5   Var_5      2877 non-null   float64\n",
      " 6   Var_6      2877 non-null   float64\n",
      " 7   HourSin    2878 non-null   float64\n",
      " 8   WeekCos    2878 non-null   float64\n",
      " 9   CBWD       2878 non-null   object \n",
      "dtypes: float64(8), object(2)\n",
      "memory usage: 225.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "info shows that this data has missing values and TimeStamp is of type object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time start: 2021-03-01 00:00:00 - time end: 2021-06-30 23:00:00\n",
      "complete_time_length: 2928 - actual_time_length: 2878\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "time_start = df['TimeStamp'].tolist()[0]\n",
    "time_end = df['TimeStamp'].tolist()[-1]\n",
    "\n",
    "complete_time_df = pd.date_range(start=time_start, end=time_end, freq='H')\n",
    "\n",
    "complete_time_length = len(complete_time_df)\n",
    "actual_time_length = len(df)\n",
    "\n",
    "print('time start: {} - time end: {}'.format(time_start, time_end))\n",
    "print('complete_time_length: {} - actual_time_length: {}'.format(complete_time_length, actual_time_length))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to statistics, if the start time of the time series is **2021-03-01 00:00:00** and the end time is **2021-06-30 23:00:00**, with hour (H) as the time frequency, there are **2928** time points in the natural series, but **2878** time points in the actual data. Therefore, during the collection of this data, some time segment values may be lost due to some factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we select variable Var_3 as the target variable to do a **univariate forecasting task**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task objective:** To forecast future infomation at 168 time points based on historical data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "Split train_data and test_data\n",
    "\n",
    "For split train_data and test_data, we can use ```sklearn.model_selection``` build-in ```train_test_split```. Here HyperTS alse provides the corresponding function ```temporal_train_test_split``` for use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts.toolbox import temporal_train_test_split\n",
    "\n",
    "df = df.drop(['Var_1', 'Var_2','Var_4','Var_5','Var_6'], axis=1)\n",
    "\n",
    "train_data, test_data = temporal_train_test_split(df, test_horizon=168)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_3</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-01 00:00:00</td>\n",
       "      <td>10.195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-01 01:00:00</td>\n",
       "      <td>3.920</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-01 02:00:00</td>\n",
       "      <td>3.318</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-01 03:00:00</td>\n",
       "      <td>3.148</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-01 04:00:00</td>\n",
       "      <td>3.215</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp   Var_3   HourSin  WeekCos CBWD\n",
       "0  2021-03-01 00:00:00  10.195  0.000000      1.0   NW\n",
       "1  2021-03-01 01:00:00   3.920  0.258819      1.0   NW\n",
       "2  2021-03-01 02:00:00   3.318  0.500000      1.0   NW\n",
       "3  2021-03-01 03:00:00   3.148  0.707107      1.0   NW\n",
       "4  2021-03-01 04:00:00   3.215  0.866025      1.0   NW"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_3</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>2021-06-30 18:00:00</td>\n",
       "      <td>10.530</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2874</th>\n",
       "      <td>2021-06-30 20:00:00</td>\n",
       "      <td>12.890</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>2021-06-30 21:00:00</td>\n",
       "      <td>12.920</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>2021-06-30 22:00:00</td>\n",
       "      <td>9.016</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>cv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>2021-06-30 23:00:00</td>\n",
       "      <td>9.970</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TimeStamp   Var_3   HourSin   WeekCos CBWD\n",
       "2873  2021-06-30 18:00:00  10.530 -1.000000 -0.222521   NW\n",
       "2874  2021-06-30 20:00:00  12.890 -0.866025 -0.222521   NW\n",
       "2875  2021-06-30 21:00:00  12.920 -0.707107 -0.222521   SE\n",
       "2876  2021-06-30 22:00:00   9.016 -0.500000 -0.222521   cv\n",
       "2877  2021-06-30 23:00:00   9.970 -0.258819 -0.222521   NE"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visulization of train_data curve and test_data curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(pd.to_datetime(train_data['TimeStamp']), train_data['Var_3'], c='gray', label='train data')\n",
    "plt.plot(pd.to_datetime(test_data['TimeStamp']), test_data['Var_3'], c='red', label='test data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Step 2: Create experiments and search for models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure some parameters for experiment. For details, see make_experiment comments.\n",
    "\n",
    "- For convenience and to avoid defining parameter writing errors, we can use the ```consts``` in ```hyperts.utils``` to get the qualified variable names.\n",
    "- In order to search for better models, we increased the number of searches by adjusting the parameter ```max_Trials```.\n",
    "- To avoid getting stuck in long rounds of invalid searches, we added ```early_stopping_rounds``` to limit the number of rounds and ```early_stopping_time_limit``` to control the total running time.\n",
    "- HyperTS is evolutionary search by default, we can change it to Monte Carlo Tree Search by adjusting the parameter ```searcher```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts.utils import consts\n",
    "from hyperts import make_experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "experiment = make_experiment(train_data=train_data.copy(),\n",
    "                task=consts.Task_UNIVARIATE_FORECAST,\n",
    "                mode=consts.Mode_STATS,\n",
    "                timestamp='TimeStamp',\n",
    "                covariables=['HourSin', 'WeekCos', 'CBWD'],\n",
    "                max_trials=30,\n",
    "                early_stopping_rounds=10,\n",
    "                early_stopping_time_limit=3600,\n",
    "                reward_metric='mae',\n",
    "                searcher='mcts')\n",
    "\n",
    "model = experiment.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Gets pipeline model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Pipeline.get_params of Pipeline(steps=[('data_preprocessing',\n",
       "                 TSFDataPreprocessStep(covariate_cols=['HourSin', 'WeekCos',\n",
       "                                                       'CBWD'],\n",
       "                                       covariate_data_clean_args={'correct_object_dtype': False,\n",
       "                                                                  'drop_columns': None,\n",
       "                                                                  'drop_constant_columns': True,\n",
       "                                                                  'drop_duplicated_columns': False,\n",
       "                                                                  'drop_idness_columns': True,\n",
       "                                                                  'drop_label_nan_rows': True,\n",
       "                                                                  'int_convert_to': 'float',\n",
       "                                                                  'nan_chars': None,\n",
       "                                                                  'reduce_mem_usage': False,\n",
       "                                                                  'reserve_columns': None},\n",
       "                                       covariate_data_cleaner_args={'correct_object_dtype': False},\n",
       "                                       freq='H', name='data_preprocessing',\n",
       "                                       timestamp_col=['TimeStamp'])),\n",
       "                ('estimator',\n",
       "                 <hyperts.hyper_ts.HyperTSEstimator object at 0x000001EED2FA1F60>)])>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_pipeline_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Forecast on test data (unknown data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, y_test = model.split_X_y(test_data.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>HourSin</th>\n",
       "      <th>WeekCos</th>\n",
       "      <th>CBWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>2021-06-23 19:00:00</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>2021-06-23 20:00:00</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>cv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>2021-06-23 21:00:00</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>2021-06-23 22:00:00</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>2021-06-23 23:00:00</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TimeStamp   HourSin   WeekCos CBWD\n",
       "2710  2021-06-23 19:00:00 -0.965926 -0.222521   NW\n",
       "2711  2021-06-23 20:00:00 -0.866025 -0.222521   cv\n",
       "2712  2021-06-23 21:00:00 -0.707107 -0.222521   NW\n",
       "2713  2021-06-23 22:00:00 -0.500000 -0.222521   NW\n",
       "2714  2021-06-23 23:00:00 -0.258819 -0.222521   NW"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>Var_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>2021-06-23 19:00:00</td>\n",
       "      <td>11.493317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>2021-06-23 20:00:00</td>\n",
       "      <td>11.875371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>2021-06-23 21:00:00</td>\n",
       "      <td>11.558684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>2021-06-23 22:00:00</td>\n",
       "      <td>10.042594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>2021-06-23 23:00:00</td>\n",
       "      <td>7.661004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TimeStamp      Var_3\n",
       "2710 2021-06-23 19:00:00  11.493317\n",
       "2711 2021-06-23 20:00:00  11.875371\n",
       "2712 2021-06-23 21:00:00  11.558684\n",
       "2713 2021-06-23 22:00:00  10.042594\n",
       "2714 2021-06-23 23:00:00   7.661004"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model.predict(X_test)\n",
    "forecast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```predict``` of HyperTS will output a DataFrame containing the predicted values of the target variables at all times in X_test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the evaluation process, we can use the ```evaluate``` function of HyperTS, which has some evaluation metrics built in by default for various tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metirc</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>1.6534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mse</td>\n",
       "      <td>4.7529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmse</td>\n",
       "      <td>2.1801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mape</td>\n",
       "      <td>0.2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>smape</td>\n",
       "      <td>0.1841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metirc  Score\n",
       "0    mae 1.6534\n",
       "1    mse 4.7529\n",
       "2   rmse 2.1801\n",
       "3   mape 0.2001\n",
       "4  smape 0.1841"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = model.evaluate(y_true=y_test, y_pred=forecast)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your own metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metirc</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean_absolute_error</td>\n",
       "      <td>1.6534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean_squared_error</td>\n",
       "      <td>4.7529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mape</td>\n",
       "      <td>0.2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Metirc  Score\n",
       "0  mean_absolute_error 1.6534\n",
       "1   mean_squared_error 4.7529\n",
       "2                 mape 0.2001"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "results = model.evaluate(y_true=y_test, y_pred=forecast, metrics=[mean_absolute_error, mean_squared_error, 'mape'])\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step6: Visualize the forecast curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HyperTS supports two ways to draw forecast curves: interactive (plotly installation required) and non-interactive (matplotlib installation required). The default is interactive. If you want to draw non-interactive, you can set ```interactive=False```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
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